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4,469 results for “elderly”
Graphs of data for elderly individuals (aged 60 to 120 years) with syphilis in Brazil
<p>A set of data graphs containing informations on elderly people with syphilis in Brazil, aged between 60-120 years with syphilis in Brazil, aged between 60-120 years and contains spreadsheet results of trend analysis of acquired syphilis, by regions of Brazil, in the period 2010-2020, referring to the article entitled "<strong>ACQUIRED SYPHILIS IN OLDER PEOPLE IN BRAZIL FROM 2010-2020".<br><br><br></strong>The dataset used to plot the graphs can be found at: <a href="https://doi.org/10.5281/zenodo.10086131">https://doi.org/10.5281/zenodo.10086131</a></p>
Exploring the Impact of Physiotherapy on Health Outcomes in Elderly Patients with Chronic Diseases: A Cross-Sectional Analysis
<p>In this cross-sectional analysis, we investigate the transformative impact of physiotherapy on health outcomes among elderly patients grappling with chronic diseases. Physiotherapy emerges as a pivotal intervention, offering multifaceted benefits that extend beyond mere symptom management. Through tailored exercises, mobility enhancements, and targeted pain management strategies, physiotherapy not only mitigates physical limitations but also fosters greater independence and quality of life. By examining a diverse cohort of elderly individuals diagnosed with chronic conditions such as osteoarthritis and cardiovascular diseases, this study underscores the profound role of physiotherapy in promoting functional mobility, reducing healthcare burdens, and enhancing overall well-being among this vulnerable population."</p>
Resting-State High-Density EEG using EGI GES 300 with 256 Channels of Healthy Elders, People with Subjective and Mild Cognitive Impairment and Alzheimer's Disease
<p>This repository contains Matlab files including 4 samples of resting-state EEG recording for Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Subjective Cognitive Decline (SCD), and Healthy Controls (HC) using the HD-EEG EGI GES 300.</p> <p><strong>[AD: i108, MCI: i100, SCD: i090, HC: s055]</strong></p> <p> </p> <p><strong>Participants & Settings</strong></p> <p>In total 230 participants have been recruited from the memory and dementia clinic of the Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD) and the 1st Department of Neurology, U.H. AHEPA, Aristotle University of Thessaloniki, Greece.</p> <p>The full dataset includes:</p> <p><strong>Healthy Controls Elders (60+ years old)</strong>: 33 participants</p> <p><strong>Subjective Cognitive Decline:</strong> 34 participants</p> <p><strong>Mild Cognitive Impairment</strong>: 79 participants</p> <p><strong>Alzheimer's Disease</strong>: 48 participants</p> <p><strong>Healthy Young (25-40 years old):</strong> 36 participants</p> <p>The study was carried out in accordance with the Declaration of Helsinki and received approval by the Scientific and Ethics Committee of GAADRD (No56_27/11/2016), and written informed consent was obtained from all participants prior to their participation in the study. The diagnosis of AD was conducted by a neuropsychiatrist according to their medical history, neuropsychological performance, structural magnetic resonance imaging (MRI), and clinical and neurological examinations.</p> <p>Participants with AD fulfilled the National Institute of Neurological and Communication Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD, as well as the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria for dementia of Alzheimer’s type (American Psychological Association, 1994). On the other hand, the MCI participants fulfilled the Petersen criteria, while the SCD group met International Working Group-2 guidelines and the recent National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease (NI-AA), as well as the SCD-I Working Group instructions. </p> <p><strong>Resting-State EEG Recording</strong></p> <p>Fifteen-minute resting EEG activity was recorded for all the participants. For the whole duration of the resting state EEG recording, participants were advised to keep themselves relaxed as much as possible, close their eyes and open them after the researcher’s demand, sit still, minimize blinking or mouth movements and let their mind wander. The experimental procedure was monitored by a research assistant aiming to identify cases of horizontal eye movements, continued blinking, or excessive movement by visually inspecting the EEG traces during the experiment. More specifically, an EEG was registered for both resting conditions (eyes open, EO and eyes closed, EC) for at least 2–3 min for each period.</p> <p><strong>EEG Data Acquisition</strong></p> <p>The EEG data were collected by using the EGI 300 Geodesic EEG system (GES 300, CERTH-ITI, Thessaloniki, Greece) with a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz (EGI Eugene, OR). Moreover, the researcher placed the electrodes in accordance with the 256 HCGSN adult 1.0 montage system, while the signals were recorded relative to a vertex reference electrode (Cz), with AFz as the ground electrode with the electrodes’ impedance below 50 kΩ throughout the experimental procedure, as recommended for the high-input impedance amplifier. In detail, the HD-EEG data were analyzed offline in order to detect any artifact, as well as to conduct pre-processing (filtering, segmentation, bad channel replacement) using Net Station 4.3 software (EGI). HD-EEG data were initially filtered with a 5th-order bandpass Butterworth IIR filter of 0.3–30 Hz. Once the segmentation was completed, the detection of artifacts was performed by using the Net Station artifact detection tool for the automatic detection of excessive eye blinking and movement. Afterward, the signals were baseline corrected using 200 msec before the start of the experiment period and average re-referenced to transform them into reference-independent values.</p> <p> </p> <p><strong>Full Dataset Access</strong></p> <p>More information about the sample dataset and access to the full dataset can be available after request via e-mail:</p> <p><strong>Ioulietta Lazarou</strong> BSc, MSc, PhD candidate</p> <p>Neuropsychologist - Clinical Research Associate </p> <p>Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)</p> <p>6th km Charilaou-Thermi Road, P.O. Box 60361, 57001 Thermi-Thessaloniki, Greece</p> <p>E-mail: <a href="mailto:iouliettalaz@iti.gr">iouliettalaz@iti.gr</a></p>
GSTRIDE: A database of frailty and functional assessments with inertial gait data from elderly fallers and non-fallers populations
<p>The GSTRIDE database contains relevant metrics and motion data of elder people for the assessment of their health status. The data correspond to 163 patients, 45 men and 118 women, between 70 and 98 years old with an average Body Mass Index (BMI) of 26.1±5.0 kg/m<sup>2</sup> and a cognitive deterioration status index between 1 and 7, according to the Global Deterioration Scale (GDS) scale. In this way, we ensure variability among the volunteers in terms of socio-demographic and anatomic parameters and their functional and cognitive capacities. The database files are stored in CSV format to ease their usability with common data processing software.</p> <p>We provide socio-demographic data, anatomical, functional and cognitive variables, and the outcome measurements from test commonly performed for the evaluation of elder people. The evaluation tests carried out to obtain these data are the Gait Speed Test (4-metre), the Hand Grip Strength, the Short Physical Performance Battery (SPPB), the Timed up and go (TUG) and the Short Falls Efficacy Scale International (FES-I). We also include the outcomes of the GDS questionnaire, the Frailty assessment and the information about falls during the last year prior to the tests.</p> <p>These data are complemented with the gait parameters of a walking test recorded by an Inertial Measurement Unit (IMU) placed on the foot. Inertial data from foot-mounted IMUs (acceleration (m/s2), angular velocity (rad/s) and timestamps (s)) are included in the database in .csv files for each participant.</p> <p>The current version includes a new gait analysis processing conducted following the methodology described in [1].</p> <p>The complete gait analysis is included for each participant and trial in .csv files, including the gait parameters estimated for all individual steps and the gait segmentation events. The gait parameters included are: cycle duration (CD) (s), cadence (steps/min), stride length (SL) (m), path length 3D (%SL), path length 2D (%SL), stride velocity (m/s), percentage of swing (%CD), percentage of stance (%CD), percentage of stance subphases (loading, foot-flat, and pushing) (%stance), heel strike pitch (degrees), toe-off pitch (degrees), peak angle velocity (degrees/s), turning angle (degrees), and heel range of motion (ROM) (degrees). </p> <p>GSTRIDE is specially focused on, but not limited to, the study of faller and non-faller elder people. The main aim of this dataset is the availability of study these different populations. By including the results of the health evaluation tests and questionnaires and the inertial and spatio-temporal data, researchers can analyze different techniques for the identification of fallers. Moreover, this database allows the analysis of cognitive deterioration and frailty parameters of patients by the research community.</p> <p>[1] L. Ruiz-Ruiz, J. J. García-Domínguez and A. R. Jiménez, "A Novel Foot-Forward Segmentation Algorithm for Improving IMU-Based Gait Analysis," in <em>IEEE Transactions on Instrumentation and Measurement</em>, vol. 73, pp. 1-13, 2024, Art no. 4010513, doi: 10.1109/TIM.2024.3449951.</p>
Healthy aging for Europe's elderly
<p>Using of Duplin Core Fields (DC) in first FAIR implementation and described in the SHAPES Collaborative Governance Model D3.5 documentation</p>
The Brain and Propranolol Pharmacokinetics in the Elderly-Figure 1.(a)Results of the Monte-Carlo simulations to describe pharmacokinetics of young patients with validation from the Taegtmeyer 2014 publication(Taegtmeyer et al., 2014)
<p>Propranolol has been found to be therapeutically effective, to obtain a clinical response by<br> beta-adrenoceptor blockade, at plasma levels of greater than 20 ng/mL(Coltart et al., 1971;<br> Frishman, 1988; Johnsson and Regàrdh, 1976). Thus, to display the data, we used highlighted<br> plasma concentration where the pharmacokinetic curve falls below 20ng/mL threshold for<br> therapeutic efficacy in the patient’s plasma.</p>
Figure 2.(a)Results of the Monte-Carlo simulations describing a population of elderly patients after a single oral dose of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>In effort to identify the recommended Propranolol dosage for elderly patients, we identified<br> the patient package inserts from the Food and Drug Administration (FDA) Inderal label, who<br> manufacture propranolol. Based from FDA Wyeth Propranolol label, for dosing in the geriatric<br> population,the label states that there were not sufficient numbers of clinical study participants who<br> were 65-years and older to properly determine the difference in response young and elderly<br> patients.</p>
Figure 6.Amygdala hypofunction after a single oral 40-mg dose, 1.5-hours post-dose, in young study participants.The image has been adapted from (Hurlemann et al., 2010).-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>In the past decade, there has been much interest in identifying treatment in adding to the<br> current treatment options for war veterans suffering from Post-Traumatic Stress Disorder (PTSD).<br> The studies investigating secondary-preventative measures for PTSD using Propranolol due to the<br> drug’s ability to inhibit the actions of the neurotransmitter norepinephrine,which has been<br> implicated to enhance the consolidation(McGhee et al., 2009; Pitman et al., 2002; Stein et al.,<br> 2007).Further, in a double-blind, placebo-controlled,functional Magnetic Resonance Imaging<br> (fMRI) study, in healthy volunteers, Hurlemann et al. found that a single oral 40mg dose of<br> propranolol attenuatedthe leftbasolateral amygdala responses to the face perception<br> paradigm(Hurlemann et al., 2010). The study participants were eighteen healthy (9 females, 9<br> males; mean age 23 years; age range 19–31 years) who had their fMRI acquisition 1.5-hours after<br> the oral administration of propranolol. An adapted image of the study findings are shown in Figure<br> 6.</p>
Figure 5.(a)Linear (y=0.45x + 57.74) dose-response relationship between plasma propranolol to % β- adrenergeric blockade derived from healthy study participants and translate into patients with angina pectoris. This image has been adapted from(Pine et al., 1975).-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Apharmacodynamic model,with parameters in the table below, may be used to visualize the<br> propranolol concentration-effect (β-blockade) relationship in patients suffering from angina pectoris.<br> These results have been adapted from the Pine et al article published in Circulation in 1975 which<br> identified a linear relationship plasma Propranolol (ng/mL) to an effect of % β-Adrenergic Blockade<br> in a single-oral dose of 40mg Propranolol in exercising individuals (Pine et al., 1975).</p>
Figure 4. Simulation (Monte-Carlo, n=200) results elderly patients taking a 10mg oral dose resulting in similar Cmax, maximum plasma concentration, to the young patients taking a 40mg oral dose. The dotted lines illustrate the 10th and 90th percentiles of plasma levels of the elderly population with a 10mg oral administration of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Thus, the package insert (see 1) recommends clinicians start at the lower end of the dosing<br> range, without further details.<br> Similarly, Pfizer manufactures Inderal® LA (Propranolol HCI), which is the long-acting<br> form of propranolol and their package insert (see 2) states, “There is no information available for<br> elderly patients.” Though the kinetics for the long-acting formdiffers from the standard form,<br> manufactured by Wyeth, we would suspect a 10mg dose for the elderly would achieve a similar<br> maximum plasma concentration (Cmax) to that of the younger patient cohort.This 10mg, which is<br> 25% of the original 40mg, dosing schedule is based on our simulations at 10mg in the geriatric<br> population.</p>
Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial (Data for independent validation study)
<p>Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.</p> <p>This dataset contains data collected during the independent validation study: derived data from raw accelerometry data, and summary performance data.</p> <p>The full dataset, including raw accelerometry data, is available here: <a href="https://mueller-et-al-2019.s3.amazonaws.com/index.html">https://mueller-et-al-2019.s3.amazonaws.com/index.html</a></p>
Extended 1.0 Dataset of "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary"
<p><strong>Introduction</strong></p> <p>We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file <em>Short_Description_of_Data_Analysis.pdf </em>and <em>Statistical_formulas.pdf </em></p> <p>The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.</p> <p><em>The dataset was expanded on <strong>23rd September 2024</strong> to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.</em></p> <p><strong>Short Description of Data Analysis and Attached Files (datasets):</strong></p> <p>Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.</p> <p>Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: <a href="https://www.ksh.hu/stadat_files/nep/hu/nep0035.html">https://www.ksh.hu/stadat_files/nep/hu/nep0035.html</a>, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the <em>Data_of_demography.xlsx</em> file.</p> <p>In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.</p> <p>The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): <a href="https://www.nnk.gov.hu/index.php/efi">https://www.nnk.gov.hu/index.php/efi</a> (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: <a href="https://semmelweis.hu/egeszsegfejlesztes/en/">https://semmelweis.hu/egeszsegfejlesztes/en/</a> (n=1). (accessed 05 Dec. 2023.)</p> <p>Geocoordinates were determined using Google Maps (N=108): <a href="https://www.google.com/maps">https://www.google.com/maps</a>. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: <em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file.</p> <p>The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:</p> <p><a href="https://qgis.org/en/site/forusers/download.html">https://qgis.org/en/site/forusers/download.html</a>. (accessed 04 Jan. 2024.)</p> <p>The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the</p> <p><em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file,</p> <p>imported via .csv file.</p> <p>The OpenStreetMap tileset is directly accessible from <a href="http://www.openstreetmap.org">www.openstreetmap.org</a> in QGIS. (accessed 04 Jan. 2024.)</p> <p>The Hungarian county administrative boundaries were downloaded from the following website: <a href="https://data2.openstreetmap.hu/hatarok/index.php?admin=6" target="_new">https://data2.openstreetmap.hu/hatarok/index.php?admin=6</a> (accessed 04 Jan. 2024.)</p> <p>HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.</p> <p>Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).</p> <p>A brief description of the statistical formulas applied is included in the <em>Statistical_formulas.pdf.</em></p> <p>Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.</p> <ul> <li>Aggregated number of HDOs by county: <em>Number_of_HDOs.xlsx</em></li> <li>Standardised data (Number of HDOs per 100,000 residents): <em>Standardized_data.xlsx</em></li> <li>Calculation of the Lorenz curve: <em>Lorenz_curve.xlsx</em></li> <li>Calculation of the Gini index: <em>Gini_Index.xlsx</em></li> <li>Calculation of the LQ index: <em>LQ_Index.xlsx</em></li> <li>Calculation of the Herfindahl-Hirschman Index: <em>Herfindahl_Hirschman_Index.xlsx</em></li> <li>Calculation of the Entropy index: <em>Entropy_Index.xlsx</em></li> <li>Regression and correlation analysis calculation: <em>Regression_correlation.xlsx</em></li> </ul> <p>Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:</p> <ul> <li>Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.</li> <li>Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv</li> <li>Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv</li> <li>Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv</li> <li>Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv</li> </ul> <p>For easier readability, the files have been provided in both SPV and PDF formats.</p> <p>The translation of these supplementary files into English was completed on 23rd Sept. 2024.</p> <p> </p> <p><em>If you have any further questions regarding the dataset, please contact the corresponding author: <a target="_new">domjan.peter@phd.semmelweis.hu</a></em></p> <p> </p>
Characteristics and challenges of elderly entrepreneurs from Cyprus. Raw dataset from 37 semi-structured interviews
<p>This dataset delves into the motivations and behaviours of elderly entrepreneurs in Cyprus - individuals who continue to engage in entrepreneurial activities past retirement age. Cypriot entrepreneurs often remain active post-retirement for varied reasons.</p> <p>The dataset includes qualitative data, collected in Cyprus (areas of Nicosia and Limassol), between the second half of 2018 and the first half of 2019. The dataset includes the transcripts in English of 37 semi-structured interviews. </p>
Genomics and Epigenomics of the Elderly Response to Pneumococcal Vaccines
ClinicalTrials.gov study NCT03104075. IPD Sharing: YES. Countries: 1. Publications: 2.
Expiratory Muscle Strength Training and Phonation Resistance Training Exercises For Elderly Patients With Vocal Fold Atrophy
ClinicalTrials.gov study NCT03696576. IPD Sharing: YES. Countries: 1. Publications: 33.
Dosage of Epidural Morphine in Elderly Patients
ClinicalTrials.gov study NCT04316871. IPD Sharing: YES. Countries: 1. Publications: 2.
The LIFE Study - Lifestyle Interventions and Independence for Elders
ClinicalTrials.gov study NCT01072500. IPD Sharing: YES. Countries: 1. Publications: 39.
Data from: Evaluating the usefulness of CogEvo for detecting early neurocognitive decline in healthy middle-aged and elderly in Japan
Open the record for dataset details and reuse information.
Background material for systematic review of literature on ICT acceptance among community dwelling elderly
<p>This is a zip file containing RIS files and PDF files that provide detailed background information about the study.</p>
Elder Wand LP
Modelo 3D de La Varita de Sauco de la saga de Harry Potter. Incluye mapa de normales y de color. ARPA BUAP. Texturizado. A 3D Model of The Elder Wand from the Harry Potter series. Includes normal map & color map. ARPA BUAP. Texturing. Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.